Aug 28, Adjust website build to work with colab badges:. Aug 15, Nov 17, Feb 23, Aug 30, Remove basemap from requirements. Jun 1, View code. Launch executable versions of these notebooks using Google Colab : Launch a live notebook server with these notebooks using binder : Buy the printed book through O'Reilly Media About The book was written and tested with Python 3.
Software The code in the book was tested with Python 3. Skip to content Home Search results for: beginning data analysis with python and jupyter book. Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction.
Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course.
You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science.
You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start. Auflage wurde der gesamte Code an Python 3. Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts such as SVM, KNN classifiers, and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Getting started with data science doesn't have to be an uphill battle.
Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book.
After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques.
You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learn Get up and running with the Jupyter ecosystem Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science.
You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start. About This Book Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Who This Book Is For This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science.
What You Will Learn Identify potential areas of investigation and perform exploratory data analysis Plan a machine learning classification strategy and train classification models Use validation curves and dimensionality reduction to tune and enhance your models Scrape tabular data from web pages and transform it into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings In Detail Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course.
Style and approach This book covers every aspect of the standard data-workflow process within a day, along with theory, practical hands-on coding, and relatable illustrations. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you will clean the dataset and after that will gain an understanding about various visualization charts.
This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms.
Each chapter contains easy-to-follow Python examples. Data Science Fundamentals 2. Installing Software and System Setup 3.
Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? Cleaning of Imported Data It allows you to create documents that contain live code, equations, and visualizations. This book is a comprehensive guide to getting started with data science using the popular Jupyter notebook.
If you are familiar with Jupyter notebook and want to learn how to use its capabilities to perform various data science tasks, this is the book for you! From data exploration to visualization, this book will take you through every step of the way in implementing an effective data science pipeline using Jupyter.
You will also see how you can utilize Jupyter's features to share your documents and codes with your colleagues. About the Instructor Paul J. Noah Gift covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow. Software engineering fundamentals tie the series together, with key instruction on linting, testing, command-line tools, data engineering APIs, and more.
He is teaching graduate machine learning and consulting on Machine Learning and Cloud Architecture for students and faculty. He has published close to technical publications, including two books on subjects ranging from Cloud Machine Learning to DevOps. Professionally, Noah has approximately 20 years of experience of programming in Python and is a member of the Python Software Foundation.
In the past ten years, he has been responsible for shipping many new products at multiple companies that generated millions of dollars of revenue and had global scale.
Im Buch steht, wie es geht. Als einer deren Agenten tot in Condors Wohnzimmer gefunden wird, scheinen alle Geheimdienste hinter ihm her zu sein. Nichts ist mehr harmlos, nichts ist unschuldig, nichts durchsichtig. Actionhaltig, sarkastisch, scharfsinnig, radikal skeptisch und enorm unterhaltsam.
0コメント